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Python [conda env:base] *
- bar chart.ipynb (58de030f)
- bar chart.ipynb
- machine Learning.ipynb (f453bbef)
- machine Learning.ipynb
- scatter plot.ipynb (1ec134ed)
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bar chart.ipynb
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- bar chart.ipynb
Kernel status: Idle Executed 2 cellsElapsed time: 1 second
[1]:
import matplotlib.pyplot as plt
import numpy as np
# Sample data: average temperatures over the past 10 years for five cities
cities = ['City A', 'City B', 'City C', 'City D', 'City E']
avg_temperatures = [22.5, 25.3, 19.8, 23.1, 21.7]
# Create a bar chart
fig, ax = plt.subplots()
bars = ax.bar(cities, avg_temperatures, color=['blue', 'green', 'red', 'purple', 'orange'])
# Add labels and title
ax.set_xlabel('Cities')
ax.set_ylabel('Average Temperature (°C)')
ax.set_title('Average Temperatures of Five Cities Over the Past 10 Years')
# Display the values on top of the bars
for bar in bars:
yval = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2, yval + 0.2, round(yval, 1), ha='center', va='bottom')
# Show the plot
plt.show()
[ ]:
#A bar chart comparing average temperatures of five cities over the past 10 years.
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